Enabling data-driven decision-making is a central theme of modern digital strategies. In this context, expectations are often placed on artificial intelligence (AI)-driven analytics to exploit the unutilized information potential of big data. However, companies regularly fall short of their expectations, as they lack the knowledge of building AI capabilities or the understanding of value-creating mechanisms. The overarching aim of this cumulative dissertation is to provide theoretical underpinnings for and empirical evidence of the mechanisms necessary to build and realize AI capabilities for data-driven value creation in an organizational context. To attain the overarching research objective, this cumulative dissertation reports on eight individual research papers embedded in a framework that builds on the big data analytics-related business value model of Grover et al. (2018). The research contributions draw on a wide range of qualitative and quantitative methods, addressing behavioral and design-oriented research questions in the field of information systems.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:osnadocs.ub.uni-osnabrueck.de:ds-202208267324 |
| Date | 26 August 2022 |
| Creators | Anton, Eduard |
| Contributors | Prof. Dr. Frank Teuteberg, Prof. Dr. Oliver Thomas |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/zip, application/pdf |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 Germany, http://creativecommons.org/licenses/by-nc-nd/3.0/de/ |
Page generated in 0.002 seconds